I don't like NumPy

If your arrays have more than two dimensions, please consider using Xarray [1], which adds dimension naming to NumPy arrays. Broadcasting and alignment then becomes automatic without needing to transpose, add dummy axes, or anything like that. I believe that alone solves most of the complaints in the article.

Compared to NumPy, Xarray is a little thin in certain areas like linear algebra, but since it's very easy to drop back to NumPy from Xarray, what I've done in the past is add little helper functions for any specific NumPy stuff I need that isn't already included, so I only need to understand the NumPy version of the API well enough one time to write that helper function and its tests. (To be clear, though, the majority of NumPy ufuncs are supported out of the box.)

I'll finish by saying, to contrast with the author, I don't dislike NumPy, but I do find its API and data model to be insufficient for truly multidimensional data. For me three dimensions is the threshold where using Xarray pays off.

[1] https://xarray.dev

Is there anything similar to this for something like Tensorflow, Keras or Pytorch? I haven't used them super recently, but in the past I needed to do all of the things you just described in painful to debug ways.

I really like einops. This works for numpy, pytorch and keras/tensorflow and has easy named transpose, repeat, and eimsum operations.